Create README.md

README.md

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+---
+language: "en"
+license: "mit"
+tags:
+- distilbert
+- sentiment-analysis
+- multilingual
+widgets:
+- text: "I love this movie!"
+---
+
+# Model Name: DistilBERT for Sentiment Analysis
+
+## Model Description
+
+### Overview
+
+This model is a fine-tuned version of `distilbert-base-uncased` on a social media dataset for the purpose of sentiment analysis. It can classify text into positive, negative, and neutral sentiments.
+
+### Intended Use
+
+This model is intended for sentiment analysis tasks, particularly for analyzing social media texts. It supports multiple languages, making it versatile for international applications.
+
+### Model Architecture
+
+This model is based on the `DistilBertForSequenceClassification` architecture, a distilled version of BERT that maintains comparable performance on downstream tasks while being more computationally efficient.
+
+## Training
+
+### Training Data
+
+The model was trained on a dataset consisting of social media posts, labeled for sentiment (positive, negative, neutral). The dataset includes multiple languages, enhancing the model's multilingual capabilities.
+
+### Training Procedure
+
+The model was trained using the following parameters:
+- Optimizer: AdamW
+- Learning Rate: 5e-5
+- Batch Size: 32
+- Epochs: 30
+
+Training was conducted on Kaggle, utilizing two GPUs for accelerated training.